Efficient implementations of Needleman-Wunsch and other sequence alignment algorithms written in Rust with Python bindings via PyO3.

Show the differences between GPX files

SneakySnake:snake: is the first and the only pre-alignment filtering algorithm that works efficiently and fast on modern CPU, FPGA, and GPU architectures. It greatly (by more than two orders of magnitude) expedites sequence alignment calculation for both short and long reads. Described in the Bioinformatics (2020) by Alser et al. https://arxiv.org/abs/1910.09020.

Needleman-Wunsch and Smith-Waterman algorithms in python

Interactive Visualization of Needleman-Wunsch Algorithm in Javascript

Collection of sequence alignment algorithms.

Collection of string similarity and distance algorithms in PHP including Levenshtein, Damerau-Levenshtein, Jaro-Winkler, and more

Generate an accurate, timestamped transcript given an audio file and its text using Google Cloud's Speech-to-Text API via gRPC.

Python implementation of several sequence alignment algorithms such as Waterman-Smith-Beyer, Gotoh, and Needleman-Wunsch intended to calculate distance, show alignment, and display the underlying matrices.

A Python module to calculate alignment between two sequences using EMBOSS' needle, stretcher, and water

Tool for exploring sequence alignment algorithms

Implementation of Needleman-Wunsch algorithm in Python Using Nested Functions.

Less-wrong single-file Numba-accelerated Python implementation of Gotoh affine gap penalty extensions for the Needleman–Wunsch, Smith-Waterman, and Levenshtein algorithms for sequence alignment

Implementation of Needleman-Wunsch, Smith-Waterman, Hirschberg and affine bioinformatics algorithms for alighning biological sequences

Cython bindings and Python interface to Opal, a SIMD-accelerated database search aligner.

The first work to provide a comprehensive survey of a prominent set of algorithmic improvement and hardware acceleration efforts for the entire genome analysis pipeline used for the three most prominent sequencing data, short reads (Illumina), ultra-long reads (ONT), and accurate long reads (HiFi). Described in arXiv (2022) by Alser et al. https://arxiv.org/abs/2205.07957

This is the implementation of 3rd Part in 3-Part Series of Algorithms Illuminated Book. All Implementations in this repository are written in both Python and Golang. Single IPython Notebook contains all Algorithms given in this Part 3.

Short implementation (~500 lines) of the Gotoh algorithm, a.k.a. Needleman-Wunsch with affine gap penalties.

A systematic survey of algorithmic foundations and methodologies across 107 alignment methods (1988-2021), for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. Described by Alser et al. at https://arxiv.org/abs/2003.00110.

DNA Sequence Alignment with Dynamic Programming Implementation using the Needleman-Wunsch Algorithm and Smith-Waterman Algorithm.

No description provided.

:microscope: :checkered_flag: Comparison of DNA Sequences

Sequence alignment algorithms

Use the Needleman-Wunsch algorithm to align two sequences: s1 and s2. Assume that a match = +2, mismatch = -2, gap = -2

This is Mispronunciation detection and diagnosis Score Metric

Dynamic Programming in Erlang.

This code is meant for educational purposes only! Sequence alignment in Python 3.x using Needleman–Wunsch algorithm. Reference code from TyMA (2017 - University of Málaga)

Sequence Alignment (Needleman–Wunsch Algorithm using Dynamic Programming) for aligning sequences (words, sentences, DNA etc.)

star alignment method implementation for multiple DNA sequences alignment

C# implementation of Needleman-Wunsch global sequence alignment algorithm.